Awesome Quant

Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.

An advanced crypto trading bot written in Python

Portfolio analytics for quants, written in Python

Free, open source, a high frequency trading and market making backtesting and trading bot, which accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books(Level-2 and Level-3), with real-world crypto trading examples for Binance and Bybit

modular quant framework.

ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem.

:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

Various Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau

Framework for quantitative trading. Complete framework for development, backtesting, and deploying automated trading algorithms and trading bots.

Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy

A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.

Python framework for quantitative financial analysis and trading algorithms on decentralised exchanges

Quantitative systematic trading strategy development and backtesting in Julia

Vanilla and exotic option pricing library to support quantitative R&D. Focus on pricing interesting/useful models and contracts (including and beyond Black-Scholes), as well as calibration of financial models to market data.

Kelly Criterion calculation

Time series implementation for the Julia language focused on efficiency and flexibility

AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.

Python API for accessing Lake high frequency tick trades & order book data

Quantitative factor research skills for AI coding assistants

Python algorithmic trading bot framework for Kubernetes: backtesting, hyperparameter optimization, 150+ technical analysis indicators (RSI, MACD, Bollinger Bands, ADX), portfolio management, PostgreSQL integration, Helm deployment, CronJob scheduling. Minimal overhead, production-ready, Yahoo Finance data.

Julia Incremental Technical Analysis Indicators (inspired by talipp)

RL stock selection for China A-share — bundled polars-native factor library (105 Alpha101 + 191 GTJA Alpha191 = 296 factors), board-aware price limits, GPU train + ONNX CPU infer, MIT-licensed.